Sea level Projections with Machine Learning using Altimetry and Climate Model ensembles

• URL: http://arxiv.org/abs/2308.02460v1
• Date: Wed, 2 Aug 2023 19:18:38 GMT
• Title: Sea level Projections with Machine Learning using Altimetry and Climate Model ensembles
• Authors: Saumya Sinha, John Fasullo, R. Steven Nerem, Claire Monteleoni
• Abstract summary: Satellite altimeter observations retrieved since 1993 show that the global mean sea level is rising at an unprecedented rate (3.4mm/year) We use machine learning (ML) to investigate future patterns of sea level change. This work presents a machine learning framework that exploits both satellite observations and climate model simulations to generate sea level rise projections.
• Score: 0.6882042556551609
• License: http://creativecommons.org/publicdomain/zero/1.0/
• Abstract: Satellite altimeter observations retrieved since 1993 show that the global mean sea level is rising at an unprecedented rate (3.4mm/year). With almost three decades of observations, we can now investigate the contributions of anthropogenic climate-change signals such as greenhouse gases, aerosols, and biomass burning in this rising sea level. We use machine learning (ML) to investigate future patterns of sea level change. To understand the extent of contributions from the climate-change signals, and to help in forecasting sea level change in the future, we turn to climate model simulations. This work presents a machine learning framework that exploits both satellite observations and climate model simulations to generate sea level rise projections at a 2-degree resolution spatial grid, 30 years into the future. We train fully connected neural networks (FCNNs) to predict altimeter values through a non-linear fusion of the climate model hindcasts (for 1993-2019). The learned FCNNs are then applied to future climate model projections to predict future sea level patterns. We propose segmenting our spatial dataset into meaningful clusters and show that clustering helps to improve predictions of our ML model.

Related papers

• OXYGENERATOR: Reconstructing Global Ocean Deoxygenation Over a Century with Deep Learning [50.365198230613956]
  Existing expert-dominated numerical simulations fail to catch up with the dynamic variation caused by global warming and human activities. We propose OxyGenerator, the first deep learning based model, to reconstruct the global ocean deoxygenation from 1920 to 2023.
  arXiv  Detail & Related papers  (2024-05-12T09:32:40Z)
• Reducing Uncertainty in Sea-level Rise Prediction: A Spatial-variability-aware Approach [4.32583920500711]
  This paper proposes a zonal regression model which addresses spatial variability and model inter-dependency. Experimental results show more reliable predictions using the weights learned via this approach on a regional scale.
  arXiv  Detail & Related papers  (2023-10-19T02:13:38Z)
• Multi-decadal Sea Level Prediction using Neural Networks and Spectral Clustering on Climate Model Large Ensembles and Satellite Altimeter Data [0.0]
  We show the potential of machine learning (ML) in this challenging application of long-term sea level forecasting. We develop a supervised learning framework using fully connected neural networks (FCNNs) that can predict the sea level trend. We also show the effectiveness of partitioning our spatial dataset and learning a dedicated ML model for each segmented region.
  arXiv  Detail & Related papers  (2023-10-06T19:06:43Z)
• Expanding Mars Climate Modeling: Interpretable Machine Learning for Modeling MSL Relative Humidity [0.0]
  We propose a novel approach to Martian climate modeling by leveraging machine learning techniques. Our study presents a deep neural network designed to accurately model relative humidity in Gale Crater. We find that our neural network can effectively model relative humidity at Gale crater using a few meteorological variables.
  arXiv  Detail & Related papers  (2023-09-04T08:15:15Z)
• Multi-scale Digital Twin: Developing a fast and physics-informed surrogate model for groundwater contamination with uncertain climate models [53.44486283038738]
  Climate change exacerbates the long-term soil management problem of groundwater contamination. We develop a physics-informed machine learning surrogate model using U-Net enhanced Fourier Neural Contaminated (PDENO) In parallel, we develop a convolutional autoencoder combined with climate data to reduce the dimensionality of climatic region similarities across the United States.
  arXiv  Detail & Related papers  (2022-11-20T06:46:35Z)
• Spatiotemporal modeling of European paleoclimate using doubly sparse Gaussian processes [61.31361524229248]
  We build on recent scale sparsetemporal GPs to reduce the computational burden. We successfully employ such a doubly sparse GP to construct a probabilistic model of paleoclimate.
  arXiv  Detail & Related papers  (2022-11-15T14:15:04Z)
• Forecasting large-scale circulation regimes using deformable convolutional neural networks and global spatiotemporal climate data [86.1450118623908]
  We investigate a supervised machine learning approach based on deformable convolutional neural networks (deCNNs) We forecast the North Atlantic-European weather regimes during extended boreal winter for 1 to 15 days into the future. Due to its wider field of view, we also observe deCNN achieving considerably better performance than regular convolutional neural networks at lead times beyond 5-6 days.
  arXiv  Detail & Related papers  (2022-02-10T11:37:00Z)
• Physics-Guided Generative Adversarial Networks for Sea Subsurface Temperature Prediction [24.55780949103687]
  Sea subsurface temperature is affected by global warming in climate change. Existing research is commonly based on either physics-based numerical models or data based models. We propose a novel framework based on generative adversarial network (GAN) combined with numerical model to predict sea subsurface temperature.
  arXiv  Detail & Related papers  (2021-11-04T23:46:51Z)
• Predicting Critical Biogeochemistry of the Southern Ocean for Climate Monitoring [1.8689461238197955]
  We train neural networks to predict silicate and phosphate values in the Southern Ocean from temperature, pressure, salinity, oxygen, nitrate, and location. We apply these models to earth system model (ESM) and BGC-Argo data to expand the utility of this ocean observation network.
  arXiv  Detail & Related papers  (2021-10-30T00:13:46Z)
• Dynamical Landscape and Multistability of a Climate Model [64.467612647225]
  We find a third intermediate stable state in one of the two climate models we consider. The combination of our approaches allows to identify how the negative feedback of ocean heat transport and entropy production drastically change the topography of Earth's climate.
  arXiv  Detail & Related papers  (2020-10-20T15:31:38Z)
• A generative adversarial network approach to (ensemble) weather prediction [91.3755431537592]
  We use a conditional deep convolutional generative adversarial network to predict the geopotential height of the 500 hPa pressure level, the two-meter temperature and the total precipitation for the next 24 hours over Europe. The proposed models are trained on 4 years of ERA5 reanalysis data from 2015-2018 with the goal to predict the associated meteorological fields in 2019.
  arXiv  Detail & Related papers  (2020-06-13T20:53:17Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.